When quoting this document, please refer to the following
URN: urn:nbn:de:0030-drops-18816
Go to the corresponding Portal

Ehler, Martin ; Geisel, Simone

Arbitrary Shrinkage Rules for Approximation Schemes with Sparsity Constraints

08492.EhlerMartin.Paper.1881.pdf (0.2 MB)


Finding a sparse representation of a possibly noisy signal is a common problem in signal representation and processing. It can be modeled as a variational minimization with $ell_ au$-sparsity constraints for $ au<1$. Applications whose computation time is crucial require fast algorithms for this minimization. However, there are no fast methods for finding the exact minimizer, and to circumvent this limitation, we consider minimization up to a constant factor. We verify that arbitrary shrinkage rules provide closed formulas for such minimizers, and we introduce a new shrinkage strategy, which is adapted to $ au<1$.

BibTeX - Entry

  author =	{Martin Ehler and Simone Geisel},
  title =	{Arbitrary Shrinkage Rules for Approximation Schemes with Sparsity Constraints},
  booktitle =	{Structured Decompositions and Efficient Algorithms},
  year =	{2009},
  editor =	{Stephan Dahlke and Ingrid Daubechies and Michal Elad and Gitta Kutyniok and Gerd Teschke},
  number =	{08492},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{},
  annote =	{Keywords: Frames, shrinkage, variational problems, sparse approximation}

Keywords: Frames, shrinkage, variational problems, sparse approximation
Seminar: 08492 - Structured Decompositions and Efficient Algorithms
Issue Date: 2009
Date of publication: 24.02.2009

DROPS-Home | Fulltext Search | Imprint Published by LZI